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1The Woolcock Institute of Medical Research, Sydney; 2The University of Sydney, Sydney; 3Department of Respiratory Medicine, Royal North Shore Hospital, Sydney; and 4Cooperative Research Centre for Asthma, Sydney, Australia
Submitted 10 April 2006 ; accepted in final form 17 April 2007
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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asthma severity; impedance
It has been suggested that the intrinsic variability of airway caliber can be measured during tidal breathing over a period of minutes (11). Respiratory system impedance (Zrs) measured using the forced oscillation technique (FOT) provides a virtually continuous measure of airway caliber throughout the breathing cycle. Que et al. (11) found that the variability in Zrs during this continuous recording was greater in asthmatic than in nonasthmatic subjects. They suggested that this variability of Zrs represents the intrinsic, spontaneous variability in airway caliber and configuration and that it might predict longer-term variability in airway caliber and thus be a useful predictor of severe or life-threatening asthma (11).
In this study, we set out to extend the findings of Que et al. (11) to determine if the short-term variability in impedance could provide clinically useful information about the severity of asthma. To do this, we first evaluated several technical variables that may have affected the measurement of impedance variability. We then compared impedance variability in asthmatic and nonasthmatic subjects and measured the relationship between impedance variability, diurnal peak flow variability, and AHR in adult subjects. In addition, we measured the effects of posture and methacholine challenge on impedance variability in nonasthmatic subjects.
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METHODS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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Asthmatic and nonasthmatic subjects aged between 17 and 75 yr were recruited from staff and students at the University of Sydney and from the Asthma Centre at Royal Prince Alfred Hospital, Sydney, Australia. Asthmatic subjects had a previous diagnosis of asthma from a respiratory physician and either had AHR or had experienced asthma symptoms requiring treatment in the past 12 mo. Nonasthmatic subjects had no history of respiratory disease, symptoms, or asthma medication use.
Data were also obtained from asthmatic and nonasthmatic subjects who participated in a follow-up study of a community-based cohort, known as the "Belmont cohort," which has been described previously (8). This cohort was established in 1982 from a random selection of 8- to 10-yr-old children attending primary schools in Belmont, New South Wales. Respiratory symptom questionnaires and histamine challenge tests (21) were performed at recruitment and then every 2 yr until age 18–20, then at age 23–25. The current assessment was undertaken when the participants were aged between 28 and 30 yr. Of the 322 subjects who participated in this follow-up, we had complete data for impedance variability and AHR from 243 participants. In this cohort, subjects were defined as asthmatic if they had experienced wheeze in the last 12 mo and had current AHR (17). Nonasthmatic subjects were defined using data both from the present study and from historical data obtained over the 20-yr follow-up of the cohort. These nonasthmatic subjects had no previous history of wheeze, respiratory disease, or use of asthma medications, and no history of having AHR over the preceding 20 yr.
Current smokers or subjects who had greater than 10 pack-years smoking history were excluded from all analyses. These studies were approved by the Human Ethics Committee of the University of Sydney, and written informed consent was obtained from all subjects.
Study Design
Effect of technical variables on Zrs variability. Respiratory system impedance was measured both in the present study and that of Que et al. (11) using noncommercial devices that were built in-house. Although all of our devices conformed to international guidelines for FOT devices (6, 19), the FOT devices used in our studies differed slightly from that described by Que et al. (11). Consequently, we first evaluated several technical variables that had the potential to affect measurements of impedance variability. Specifically, we compared impedance variability measured by a device that included an inertance tube, as described by Que et al. (11), and by our device that had a flow splitter plus a resistance mesh. In addition, we examined the effect on impedance variability of using pneumotachographs with different dead spaces (Fleisch, dead space 130 ml; and Hans Rudolph, dead space 60 ml) and of using oscillatory signals of high (±2.5 cmH2O) and low (±0.8 cmH2O) amplitude. Since many subjects found it difficult to breathe on a mouthpiece continuously for 15 min, we examined the validity of reducing the duration of the measurement by comparing impedance variability measured during the first minute of recording with that measured during the whole 15-min period. These studies required multiple experiments involving, in total, 17 nonasthmatic and 15 asthmatic subjects. Not all subjects participated in all experiments.
Clinical implications of short-term Zrs variability. The clinical implications of short-term variability of impedance were assessed in three populations. In the Belmont cohort, we compared short-term variability of impedance in 82 nonasthmatic and 17 asthmatic adults, and we examined the relationship between short-term variability of impedance and AHR to histamine. Second, in a group of 28 asthmatic subjects and seven nonasthmatic subjects, we examined the relationship between short-term variability of impedance and diurnal variability of PEF measured over 2 wk. Finally, in eight nonasthmatic subjects, we examined the effect of airway smooth muscle activation and changes in posture on short-term variability of impedance. In this study, Zrs was measured during methacholine challenge on two separate days, in upright and supine positions.
In all studies subjects withheld short-acting 
2-agonist for 6 h and long-acting 2-agonists for 24 h before testing. Asthma symptoms, medication use, and smoking history were obtained by self-completed questionnaire. Lung function was measured by spirometry according to American Thoracic Society criteria (1). Impedance was measured by the FOT as described below and was always measured before spirometry.
FOT and Data Processing
Zrs was measured continuously using the FOT during tidal breathing, from which the mean and variability of the absolute values of Zrs were calculated. The FOT device has been described previously (15, 16). Briefly, a 6-Hz oscillation, generated by a loudspeaker, was applied to the airway opening via a three-way flow splitter that allowed the subject to breathe normally from room air. The three openings of the splitter were attached to the distal opening of the pneumotachograph, to atmosphere, and to the loudspeaker. Flow was measured using either a Fleisch (50 mm diameter, Phipps and Bird) or Hans Rudolph (model 4830, flow range 0–400 l/min, Hans Rudolph, VacuMed, Ventura, CA) pneumotachograph. Differential pressure was measured using a solid-state pressure transducer with range ±2.5 cmH2O (Sursense DCAL4, Honeywell). Pressure at the airway opening was measured using a similar transducer that had a higher range (±12.5 cmH2O). The pressure and flow signals were low-pass filtered at 25 Hz and sampled at 300 Hz with a 16-bit analog-to-digital converter. Signals were then filtered using a bandwidth of 3 Hz centered around the oscillation frequency 6 Hz to reduce potential leakage from nonharmonic frequency components in the recorded signal. The filtered signals were then divided into segments exactly equal in duration to the period of the oscillation signal (1/6 s) and overlapping so that each segment started 0.05 s from the previous one (i.e., 20 segments/s). The impedance and its real (resistance) and imaginary (reactance) parts were derived from this sequence of segments by Fourier analysis. This entailed estimation of the cross-spectrum and power spectra from three adjacent segments of the pressure and flow signals. Zrs, calculated at intervals of 0.1 s (i.e., 10 estimates/s), was then defined as the 6-Hz component of the ratio between the pressure power spectrum and the pressure-flow cross-spectrum estimates (16).
Since the period of the oscillation component is exactly equal to the length of the Fourier transform, tapered windows are not required (or desirable), but reducing nonharmonic components (in particular the large low-frequency breathing component) is essential to avoid leakage of their power into the desired frequency component. Filters (type II Chebychev) were applied in both forward and time-reversed directions to minimize phase distortions of the signals.
Erroneous and extreme Zrs values, which could have occurred if the glottis closed or the seal around the mouthpiece was lost during testing, were identified and excluded using a procedure identical to that described by Que et al. (11). Each measurement of impedance was plotted against flow, and large outlying data points at zero flow, as well as all negative respiratory system resistance (Rrs) values, were manually excluded from analysis. Resistance was reported as percentage of the predicted values of Pasker et al. (7).
To examine the effect of the inertance tube on short-term variability, we attached a 3.8-m-long tube with a 35-mm diameter onto the expiratory port of the FOT. Bias flow was applied at a constant rate of 12 l/min.
To examine the effect of different oscillatory signal pressures, the flow head was occluded, and the peak pressures were adjusted to ±2.5 cmH2O for high-amplitude and ±0.8 cmH2O for low-amplitude measurements.
Diurnal Variability
PEFs and forced expiratory volume in 1 s (FEV1) were measured twice a day at home for 2 wk using a hand-held turbine-style electronic spirometer (MicroMedical DiaryCard, Rochester, Kent, UK). Three spirometric maneuvers were recorded immediately on waking and before sleep, and the best PEF and FEV1 of each set of three were selected. The diary displayed only PEF in liters per minute but also stored date, time, flow volume curves, FEV1, and forced vital capacity (FVC) for later review.
Individual PEF and FEV1 data points were examined and, for values that were 1.5 SDs outside the individual patient mean, the stored flow-volume loops were reviewed. Data from artefactual maneuvers, and from maneuvers where FEV1 equaled FVC, were excluded from analysis (13).
Variability in airway caliber was calculated as within-day variability (diurnal variability) for both PEF and FEV1. Diurnal variability was calculated for each day as amplitude percent maximum, i.e., the absolute difference between the morning and evening value, expressed as a percentage of the highest value for the day. The overall diurnal variability was the mean of the 2-wk period (14). Data sets with <50% of days with both morning and evening measurements were excluded from the final analysis.
Airway Responsiveness
In the Belmont cohort, airway responsiveness was measured using histamine diphosphate, administered via a DeVilbiss no. 45 hand-held nebulizer (DeVilbiss HealthCare, Somerset, PA) in doses ranging from 0.03 to 3.9 µmol histamine. Subjects with baseline FEV1 below 70% predicted underwent a bronchodilator reversibility test with 200 µg salbutamol. AHR was defined as a provocative dose of 
3.9 µmol histamine producing a 20% fall in FEV1, or bronchodilator response 
15% increase in FEV1.
Dose-response slope was calculated as percent fall in FEV1 per micromole histamine (5, 8) to provide a continuous variable for airway responsiveness for use in correlation analyses.
In nonasthmatic subjects, the effect on impedance variability of smooth muscle activation and posture was measured during methacholine challenge in the upright and supine position. In this study, methacholine was administered via a DeVilbiss no. 646 nebulizer using a nebulization dosimeter (Rosenthal French, Baltimore, MD) attached to oxygen at 138 kPa in doubling doses, ranging from 0.15 to 199 µmol.
Data Analysis
All data were analyzed using Analyse-It for Excel (Analyse-It Software, Leeds, UK). Individual frequency distribution curves of Zrs and lnZrs were constructed using bin sizes of 0.2 cmH2O·l–1·s, with frequency normalized by expressing it as a percentage of the total number of measurements. The Kolmogorov-Smirnov statistic was calculated for individual subjects using Zrs and the natural logarithm of Zrs and is reported as the mean of the individual values. Comparisons of the Kolmogorov-Smirnov values were made using the Wilcoxon signed ranks test. The SD of lnZrs (lnZrsSD) was used as the primary measure of the variability of Zrs (9). Results are expressed as means ± SD unless otherwise stated. Correlations were examined using either Pearson's (r) or Spearman's (rs) correlation coefficient. Comparisons were made using paired or unpaired t-tests with a significance level of 0.05.
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RESULTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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Figure 1 shows typical traces of Zrs measured over 15 min in an asthmatic and a nonasthmatic subject. The distribution of both Zrs and lnZrs, in asthmatic and nonasthmatic subjects, deviated significantly from a Gaussian distribution (Table 1). However, lnZrs more closely approximated a normal distribution than did Zrs as indicated by lower Kolmogorov-Smirnov values for lnZrs obtained using both the high-amplitude oscillations (difference in mean Kolmogorov-Smirnov statistic, P = 0.01) and the low-amplitude oscillations (P = 0.001). Figure 2 shows individual normalized frequency distributions of lnZrs for both asthmatic and nonasthmatic subjects. There were no significant differences between asthmatic and nonasthmatic subjects in the small proportion of data points excluded because of error or artefact (P = 0.69, high amplitude; P = 0.17, low amplitude).
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The major technical difference between the FOT device used by Que et al. (11) and our FOT device was the inclusion of an inertance tube rather than the flow splitter and resistance mesh used in our device. We compared lnZrsSD collected over 15 min in five asthmatic and six nonasthmatic subjects using both methods. There was no significant difference in lnZrsSD between asthmatic and nonasthmatic subjects using either the inertance tube (0.27 ± 0.08 vs. 0.33 ± 0.04, P = 0.19) or the resistance mesh (0.33 ± 0.06 vs. 0.30 ± 0.04, P = 0.32). In these subjects, compared with nonasthmatic subjects, asthmatic subjects had lower FEV1 (86.6 ± 19.9 vs. 102.8 ± 11.3% predicted, P = 0.12) and higher Rrs (115.7 ± 54.0 vs. 75.1 ± 21.7% predicted, P = 0.15), but the differences were not significant.
Effect of Oscillatory Amplitude and Pneumotachograph Device
In asthmatic subjects there were no significant differences between low- and high-amplitude oscillations in lnZrsSD, Rrs, or Zrs (Table 2). However, in nonasthmatic subjects, high-amplitude oscillations decreased lnZrsSD but had no effect on Rrs or Zrs. There were no significant differences in lnZrsSD between the Fleisch and Hans Rudolph devices in either asthmatic (P = 0.74) or nonasthmatic (P = 0.79) subjects at high amplitude. There were no significant differences in lnZrsSD between asthmatic and nonasthmatic subjects at either high (P = 0.33, Fleisch; P = 0.21, Hans Rudolph) or low (P = 0.54, Fleisch; P = 0.61, Hans Rudolph) amplitude. In this group of subjects, compared with nonasthmatic subjects, asthmatic subjects had lower FEV1 (87.3 ± 17.1 vs. 104.7 ± 14.2% predicted, P = 0.04), and higher Rrs (84.8 ± 31.2% vs. 121.38 ± 43.7% predicted, P = 0.06).
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To determine if the duration of the impedance measurements could be reduced, we compared the distribution and variability of lnZrs calculated from the full 15-min recording with data calculated from only the first minute of recording, using combined data from asthmatic and nonasthmatic subjects. The lnZrs data were not normally distributed over 1 min; however, the distribution was closer to normal than the distribution of measurements recorded over 15 min (Table 1). There were no significant differences in lnZrsSD between the 1- and 15-min recordings in either asthmatic or nonasthmatic subjects Table 1).
The variability of lnZrs, measured as lnZrsSD, did not differ significantly between asthmatic and nonasthmatic subjects (P = 0.68, high amplitude; P = 0.32, low amplitude). Furthermore, there was no significant difference in the variability of resistance, measured by lnRrsSD, between asthmatic and nonasthmatic subjects (P = 0.99, high amplitude; P = 0.26, low amplitude). In these asthmatic subjects, compared with nonasthmatic subjects, FEV1 was significantly lower (85.0 ± 14.6 vs. 103.2 ± 14.3% predicted, P = 0.004) and Rrs was significantly greater (124.6 ± 39.0 vs. 89.8 ± 25.5% predicted, P = 0.01).
The variability of impedance, before natural log transformation, measured by ZrsSD, was strongly dependent on airway caliber (Fig. 3). In measurements over 15 min and those over 1 min, there was a highly significant correlation between Zrs and ZrsSD in both asthmatic (r = 0.73, P = 0.005, over 15 min; r = 0.91, P < 0.0001, over 1 min) and nonasthmatic (r = 0.90, P < 0.0001, 15 min; r = 0.81, P < 0.0001, 1 min) subjects (Fig. 3, A and C). However, after log conversion, the correlation between lnZrs and lnZrsSD was significantly diminished in both asthmatic (r = –0.06, P = 0.85, 15 min; r = 0.38, P = 0.02, 1 min) and nonasthmatic (r = 0.32, P = 0.28, 15 min; r = 0.40, P < 0.001, 1 min) (Fig. 3, B and D) subjects.
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To determine if we could detect differences in impedance variability between asthmatic and nonasthmatic subjects, impedance was measured over 1 min using high-amplitude oscillations and a Fleisch pneumotachograph in the Belmont cohort (Table 3). Asthmatic and nonasthmatic subjects differed significantly in FEV1, Zrs, and Rrs. However, there was no significant difference in lnZrsSD between asthmatic and nonasthmatic subjects (Table 3). Furthermore, there was no significant correlation between lnZrsSD and AHR, measured by dose-response slope, in either asthmatic (r = 0.10, P = 0.71) or nonasthmatic subjects (r = –0.12, P = 0.27) (Fig. 4). Two of the 17 asthmatic subjects could not have AHR to histamine measured since baseline FEV1 was less than 70% predicted.
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The association between short-term variability, measured by lnZrsSD over 1 min, and diurnal variability of PEF and FEV1, measured over 2 wk, was assessed in 28 asthmatic subjects and 7 nonasthmatic subjects (Table 4). As expected, the diurnal variability of airway caliber was greater in asthmatic than nonasthmatic subjects, measured either by PEF (8.1 ± 4.8% vs. 3.5 ± 1.2%, P = 0.02) or FEV1 (7.6 ± 5.2% vs. 2.9 ± 1.4%, P = 0.06). However, there was no significant difference in lnZrsSD between asthmatic and nonasthmatic subjects, and, in asthmatic subjects, lnZrsSD did not correlate with the diurnal variability of either PEF (rs =–0.12, P = 0.54) (Fig. 5) or FEV1 (rs =–0.04, P = 0.86).
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Mean values for lnZrs and lnZrsSD in eight nonasthmatic subjects at baseline and after methacholine challenge in the upright and supine positions are shown in Table 5. Compared with baseline in the upright position, lnZrs was increased both by supine position and by methacholine challenge. However, lnZrsSD was not changed from the baseline upright values by either supine position or methacholine challenge. In this group of nonasthmatic subjects, both FEV1 (100.3 ± 11.4% predicted) and Rrs (82.5 ± 30.5% predicted) were within the normal predicted range.
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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The principles of the FOT are well established (2), and our measurement device followed the same general design as systems described previously by ourselves (15, 16) and recommended guidelines for measurement of impedance (6, 19). However, there were some differences between our FOT devices and the device used by Que et al. (11). Systematic evaluation of several technical factors showed that neither the use of a resistance mesh, instead of an inertance tube, nor the use of a Hans Rudolph, rather than a Fleisch, pneumotachograph altered impedance or impedance variability. The only factor that was found to affect impedance variability was the amplitude of the pressure oscillations. We found that, in a small group of nonasthmatic subjects, the variability of impedance was reduced with a high-amplitude oscillation, consistent with an improvement in signal-to-noise ratio. Therefore, our clinical measurements of impedance variability were made using a high-amplitude oscillation.
To increase the practicality of the measurement of impedance variability for use in the outpatient clinic and in epidemiological field studies, we examined the effect of reducing the duration of the measurement from 15 min to 1 min. Our data confirm the observation by Que et al. (11) that over 15 min, the distribution of lnZrs is nearly, but not quite, log-normally distributed, and abbreviating the test to 1 min brought the distribution closer to a log-normal distribution. There were no significant differences in impedance variability between measurements made over 15 min and 1 min in either asthmatic or nonasthmatic subjects. This observation, and the finding that impedance variability measured over 15 min did not differ significantly between asthmatic and nonasthmatic subjects, suggests that the differences in findings between our study and that of Que et al. (11) cannot be attributed to differences in the duration of the measurement. The data-processing methods, including that used to remove artefacts due to glottal closure, swallowing, and leaks, were similar in our study and that of Que et al. (11). In both studies, plots of Zrs against flow were used to identify and exclude large values of Zrs at zero flow. There were no significant differences between asthmatic and nonasthmatic subjects in the percentage of data excluded as artefacts in either study.
To investigate the clinical significance of impedance variability, impedance was measured over 1 min using a high-amplitude oscillation at 6 Hz in asthmatic and nonasthmatic subjects. Impedance variability was not significantly different in asthmatic and nonasthmatic subjects, although there was a nonsignificant trend (P = 0.09) for increased impedance variability in asthmatic subjects in the Belmont cohort. This raises the possibility that impedance variability might provide clinically useful information in some populations. However, the magnitude of the difference in impedance variability between asthmatic and normal subjects in the Belmont cohort (0.24 in normal subjects and 0.27 in asthmatic subjects) was smaller than the difference seen in the study by Que et al. (11) (0.24 in normal subjects and 0.34 in asthmatic subjects). More importantly, there was a very large overlap in impedance variability between normal and asthmatic subjects, suggesting that the measurement would have no diagnostic utility. Furthermore, we found that impedance variability was not related to diurnal variability of airway caliber or to AHR over a wide range of severity. These findings suggest that although it may be possible to detect differences in impedance variability between asthmatic and normal subjects in some samples, these differences are inconsistent, and impedance variability is not related to other well-validated markers of asthma.
We and others have previously examined the source of variability in Rrs values, hence Zrs values, during normal tidal breathing (10, 16). Much of the variability is directly related to both fluctuations in flow and in volume, both of which have been long recognized (20). The changes in volume are likely due to direct increases in caliber due to changes in transmural pressure generated by the swings in pleural pressure, albeit small, during tidal breathing. Thus it is possible that alterations in the rate and depth of breathing could affect impedance variability.
The subjects in our studies were well characterized. The Belmont cohort is a general population sample of young adults (9), and impedance measurements were made in 243 subjects during a follow-up study of this cohort when they were aged 28 to 30 yr. We set very strict criteria to define both nonasthmatic and asthmatic subjects to maximize the chance of detecting any differences in impedance variability. Of these, we identified 82 subjects that met our strict criteria for nonsmoking, nonasthmatic controls and 17 that met the criteria for nonsmoking asthmatic subjects. Similarly, the 28 asthmatic subjects recruited from the outpatient clinic were well characterized and had clinically stable asthma that ranged in severity from intermittent to severe asthma, on the basis of the Global Initiative for Asthma (GINA) guidelines (3). In the study of Que et al. (11), 7 of the 10 subjects were said to have "worsening" asthma, implying clinical instability, although the clinical criterion for worsening asthma was not defined, and no details of medication use were given. In asthmatic subjects, resting lung function, measured by mean FEV1% predicted, and by Zrs was similar in our outpatient group and those studied by Que et al. (11). In the nonasthmatic controls in both the Belmont cohort and the laboratory studies, mean resistance values were close to normal predicted values (7), but mean Zrs was a little higher than the equivalent measurement in control subjects in the study of Que et al. (11). No anthropometric data, or measurements of resistance relative to predicted values, were provided for the nonasthmatic subjects in the study by Que et al. (11), so it is not clear whether their subjects were similar to ours. Thus, although the inclusion criteria for nonasthmatic and asthmatic subjects were similar in our study and that of Que et al. (11), it is difficult to compare the subjects in the two studies, and it is possible that the differences in the findings of the two studies could be attributed to differences in subject characteristics.
Que et al. (11) suggested that an increase in short-term variability in asthmatic subjects might be due to airway smooth muscle activation combined with unloading of the airways, possibly resulting from peribronchial inflammation. They mimicked this condition in nonasthmatic subjects using methacholine challenge to cause airway smooth activation and supine posture to cause unloading and reported that the combination increased variability into the asthmatic range. We reasoned that even if the differences between the studies were due to difference in subject selection, we should still be able to detect a change in Zrs variability by changing posture and smooth muscle activation. However, we could not reproduce the findings of Que et al. (11) and found no changes in lnZrsSD in the supine position or after methacholine challenge. This suggests that the difference between studies may be more fundamental than differences in subject selection.
The importance of using log-transformed, rather than raw, Zrs values to measure impedance variability was highlighted both in our study and in that of Que et al. (11). Both studies show clearly that the distribution of Zrs values is closer to log-normal than to a normal Gaussian distribution. Thus the SD of the raw Zrs values is not a true representation of the variance of such a skewed distribution. Furthermore, we have shown that there is a very close correlation between mean Zrs and its SD, suggesting that the variability, measured by ZrsSD, is highly dependent on airway caliber. This effect is probably due, in part, to a geometric amplification of small variations in airway resistance in airways of small diameter. While log transformation substantially diminished the association between caliber and variability both in our study and in that of Que et al. (11), other studies have not made this adjustment. A recent study in children (18), in which the short-term variability of airway caliber differed between asthmatic and nonasthmatic children, did not use log-transformed data and did not make any analysis of the association between variability and airway caliber. It is likely that the variability measured in this way is simply a proxy for differences in airway caliber. It has not been established whether the variability in airway caliber provides any additional, clinically relevant information other than that available from airway caliber alone.
A simple and rapid test to measure airflow variability would be valuable for diagnosis and monitoring of asthma. Airflow variability is determined by a combination of factors that include not only the intrinsic airway dynamics and small momentary changes in airway caliber but also larger-scale environmental factors, such as circadian rhythms and allergen exposure. The hypothesis that the intrinsic variability of the airways can predispose the airways to be more susceptible to the larger-scale factors has great appeal. Although the study reported by Que et al. (11) implied that a test of intrinsic variability might be possible, in our studies we were unable to reproduce their findings. Using standard measurement and data-processing methods, and systematically eliminating any effects of equipment or protocol between the studies, we found no difference in the variability of impedance during tidal breathing between asthmatic and nonasthmatic subjects. Furthermore, the variability of impedance did not relate to measures of asthma severity such as AHR and PEF variability and was not affected by posture or smooth muscle activation. Although we have not been able to identify the source of the differences between our findings and those of Que et al. (11), our findings in large populations of well-characterized asthmatic and nonasthmatic subjects do not support the view that measurement of short-term variability of airway caliber can provide clinically useful information about airway status.
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TOPABSTRACTMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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ACKNOWLEDGMENTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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) and 13 nonasthmatic (
) subjects (A and B) and 1 min of tidal breathing in 38 asthmatic and 102 nonasthmatic subjects (C and D). A and C show raw Zrs values, and B and D show values obtained after natural log transformation.
